Reconfigurable intelligent surfaces (RISs) are capable of enhancing the capacity of wireless networks at a low cost. In practical RIS-assisted communication systems, the acquisition of channel state information (CSI) and RIS reflection optimization constitute a pair of challenges. In this paper, a lowcomplexity channel estimation and passive beamforming design is proposed. First of all, we conceive a low-complexity framework for maximizing the achievable rate of RIS-assisted multipleinput multiple-output (MIMO) systems having discrete phase shifts at each RIS element. In contrast to existing solutions, the proposed arrangement partitions the channel training stage into several phases, where the RIS reflection coefficients are pre-designed and the effective superposed channel is estimated instead of separately training the source-destination and source-RIS-destination links. Based on this, the active beamformer can be designed at low complexity and the RIS reflection optimization is performed by selecting that one from the pre-designed training set which maximizes the achievable rate. Secondly, we propose novel techniques for generating the training set of RIS reflection coefficients. The theoretical performance of the proposed scheme is analyzed and compared to the optimal RIS configuration. Finally, our simulation results demonstrate that the proposed framework is more competitive than its existing counterparts when relying on imperfect CSI, especially for rapidly timevarying channels having short channel coherence time.

In Reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information (CSI) and the optimization of the reflecting coefficients constitute a pair of salient design issues. In this paper, a novel channel training protocol is proposed, which is capable of achieving a flexible performance vs. signalling and pilot overhead as well as implementation complexity trade-off.More specifically, first of all, we conceive a holistic channel estimation protocol, which integrates the existing channel estimation techniques and passive beamforming design. Secondly, we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods, where the superimposed end-to-end channel is estimated instead of separately estimating the direct BS-user channel and cascaded reflected BS-RISuser channels. As a result, the reflecting coefficients of the RIS are optimized by comparing the objective function values over multiple training periods. Moreover, the theoretical performance of our channel training protocol is analyzed and compared to that under the optimal reflecting coefficients. In addition,

Next-generation communication systems aim for providing pervasive services, including the high-mobility scenarios routinely encountered in mission-critical applications. Hence we harness the recently-developed reconfigurable intelligent surfaces (RIS) to assist the high-mobility cell-edge users. More explicitly, the passive elements of RISs generate beneficial phase rotations for the reflected signals, so that the signal power received by the high-mobility users is enhanced. However, in the face of high Doppler frequencies, the existing RIS channel estimation techniques that assume block fading generally result in irreducible error floors. In order to mitigate this problem, we propose a new RIS channel estimation technique, which is the first one that performs minimum mean square error (MMSE) based interpolation for the sake of taking into account the timevarying nature of fading even within the coherence time. The RIS modelling invokes only passive elements without relying on RF chains, where both the direct link and RIS-reflected links as well as both the line-of-sight (LoS) and non-LoS (NLoS) paths are taken into account. As a result, the cascaded base station (BS)-RIS-user links involve the multiplicative concatenation of the channel coefficients in the LoS and NLoS paths across the two segments of the BS-RIS and RIS-user links. Against this background, we model the multiplicative RIS fading correlation functions for the first time in the literature, which facilitates MMSE interpolation for estimating the high-dimensional and high-Doppler RIS-reflected fading channels. Our simulation results demonstrate that for a vehicle travelling at a speed as high as 90 mph, employing a low-complexity RIS at the cell-edge using as few as 16 RIS elements is sufficient for achieving substantial power-effieincy gains, where the Doppler-induced error floor is mitigated by the proposed channel estimation technique.

Reconfigurable intelligent surfaces (RIS) constitute a revolutionary technique of beneficially reconfiguring the smart radio environment. However, despite the fact that wireless propagation is of time-varying nature, most of the existing RIS contributions focus on time-invariant scenarios for the following reasons. Firstly, it becomes impractical to instantaneously feed back the control signal based on the doubly selective non-line-ofsight (NLoS) fading scenario. Secondly, channel estimation conceived for the high-mobility and high-dimensional RIS-assisted links has to take into account the spatial-domain (SD), timedomain (TD), and frequency-domain (FD) correlations imposed by the angle-of-arrival/departure (AoA/AoD), the Doppler and the orthogonal frequency-division multiplexing (OFDM) operations, respectively, where none of the existing solutions can be directly applied. Thirdly, it is far from trivial to maximize the NLoS channel powers on all subcarriers by a common set of RIS reflecting coefficients. Fourthly, in the face of double selectivity, it becomes inevitable to encounter either inter-symbol interference (ISI) or inter-channel interference (ICI) during the signal detection in the TD or in the FD, respectively. Against this background, firstly, we focus our attention on line-of-sight (LoS) dominated unmanned aerial vehicle (UAV) scenarios. Secondly, we conceive new minimum mean squared error (MMSE) channel estimation methods for doubly selective fading, which perodically transmit pilot symbols embedded into the TD and FD over the SD in order to beneficially exploit the correlations in the three domains. Thirdly, the RIS coefficients are optimized by a low-complexity algorithm based on the LoS representation of the end-to-end system model. Fourthly, tailor-made interference cancallation techniques are devised for improving the signal detection both in the FD and in the TD. Our simulation results are examined in six frequency bands licensed in 5G, which confirms that the employment of RIS is capable of achieving substantial performance improvements.

Intelligent reflecting surfaces (IRS) have emerged as a promising technology of managing the radio signal propagation by relying on a large number of low-cost passive reflecting elements. In this letter, the optimal pilot power allocation required for accurate channel estimation of IRS-assisted communication systems is investigated. In contrast to conventional channel estimators, where pilot signals are usually designed to be constant-enveloped, we reconsider the pilot design to improve the passive beamforming performance thus resulting in an improved achievable rate. At first sight the result of our analysis appears counter-intuitive, suggesting that at a given total power, more power should be allocated to estimate low-gain channels, since the channel phase impairments are more severe than those of highgain channels. Our simulation results show that when the number of IRS elements is 4, the rate improvement of our proposed channel estimation scheme over the conventional counterpart may be as high as 25%.